Topological Feature Selection: A Graph-Based Filter Feature Selection Approach. (arXiv:2302.09543v2 [cs.LG] UPDATED)
In this paper, we introduce a novel unsupervised, graph-based filter feature
selection technique which exploits the power of topologically constrained
network representations. We model dependency structures among features using a
family of chordal graphs (the Triangulated Maximally Filtered Graph), and we
maximise the likelihood of features' relevance by studying their relative
position inside the network. Such an approach presents three aspects that are
particularly satisfactory compared to its alternatives: (i) it is highly
tunable and easily adaptable to the nature of input data; (ii) it is fully
explainable, maintaining, at the same time, a remarkable level of simplicity;
(iii) it is computationally cheaper compared to its alternatives. We test our
algorithm on 16 benchmark datasets from different applicative domains showing
that it outperforms or matches the current state-of-the-art under heterogeneous
evaluation conditions.